Quantitative Methods for Food Systems Research

Chris Donovan

Food Systems Data Scientist
Food Systems Research Institute

November 5, 2025

Outline

  1. Quant methods, what they are good for and what they aren’t
  2. Things I wish someone told me earlier
  3. Food Systems Secondary Data Survey

Quantitative Methods in Food Systems

Quantitative Methods in Food Systems

  • Old friends
    • T-test
    • ANOVA
    • Regression
  • Broader frameworks
    • Dimension reduction
    • Factor analysis
    • Structural equation model
  • System modeling
    • Fuzzy cognitive mapping
    • Network
    • Agent-based
    • System dynamics
Description of image
Models identified from a review of studies on retail food environments (Mui et al. 2025)
Description of image
Share of publications over time in Agricultural complex system modeling (monasterolo2016SustainableInclusiveFood?)

(Mis-)Interpreting Quantitative Results

A picture of a field at Intervale Farm.

Intervale Farm, Sally McCay, UVM Photo

Things I wish someone told me earlier

  • Whiteboard exercise
  • Think through:
    • Power, sample size, outliers
  • Consider preregistration
  • GUI vs scripting
  • R, Python, Julia, others
  • Open source
  • Free (both ways)
  • Do it
  • Get to know your data
  • Distributional assumptions
  • Missing data

  • Reproducibility

  • Basically Git

  • Add the comic

  • Maria S. at the library
  • DataCamp, other online tools
  • Use AI scrupulously
  • Each other!

Closing thoughts on Quant things

  • Participatory modeling
    • FCM, Community system dynamics
  • Even if you’re not going to do quant analysis, you have to know enough to critically consume

Data Survey Title Slide?

Intro

Methods

  • Data sources
    • USDA: NASS, ERS, USFS
    • BLS
    • Census Bureau
  • Analyses
    • Spatial regression. Why error and not lag

\[ Y = mx + b \]

Results

  • Graph options
    • Indicators over time
    • Correlation matrix (probably not)
    • Trends
  • Probably do indicators over time
  • Lack of social indicators

Discussion

  • Value in monitoring
  • Social data
  • Open, FAIR, easily accessible
  • What are data good and not good for

References

Anderson, Samantha F. 2020. “Misinterpreting p: The Discrepancy Between p Values and the Probability the Null Hypothesis Is True, the Influence of Multiple Testing, and Implications for the Replication Crisis.” Psychological Methods 25 (5): 596–609. https://doi.org/10.1037/met0000248.
Cumming, Geoff, and Sue Finch. 2005. “Inference by Eye: Confidence Intervals and How to Read Pictures of Data.” American Psychologist 60 (2): 170–80. https://doi.org/10.1037/0003-066X.60.2.170.
Mui, Yeeli, Megan R. Winkler, Shanda L. Hunt, Joel Gittelsohn, and Melissa Tracy. 2025. “Simulated Retail Food Environments: A Literature Review of Systems Science Approaches to Advance Equity in Access to Healthy Diets.” Obesity Reviews 26 (5): e13887. https://doi.org/10.1111/obr.13887.
Zhang, Sam, Patrick R. Heck, Michelle N. Meyer, Christopher F. Chabris, Daniel G. Goldstein, and Jake M. Hofman. 2023. “An Illusion of Predictability in Scientific Results: Even Experts Confuse Inferential Uncertainty and Outcome Variability.” Proceedings of the National Academy of Sciences 120 (33): e2302491120. https://doi.org/10.1073/pnas.2302491120.